Apparatus and method for dual-energy computed tomography (CT) image reconstruction using sparse kVp-switching and deep learning

ABSTRACT

A deep learning (DL) network reduces artifacts in computed tomography (CT) images based on complementary sparse-view projection data generated from a sparse kilo-voltage peak (kVp)-switching CT scan. The DL network is trained using input images exhibiting artifacts and target images exhibiting little to no artifacts. Another DL network can be trained to perform image-domain material decomposition of the artifact-mitigated images by being trained using target images in which beam hardening is corrected and spatial variations in the X-ray beam are accounted for. Further, material decomposition and artifact mitigation can be integrated in a single DL network that is trained using as inputs reconstructed images having artifacts and as targets material images without artifacts with beam-hardening corrections, etc. Further, the target material images can be transformed using a whitening transform to decorrelate noise.

FIELD

This disclosure relates to using deep learning (DL) networks toreconstruct a computed tomography (CT) image from dual-energy (DE)sparse kilo-voltage peak (kVp)-switching projection data.

BACKGROUND

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent the work is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

Computed tomography (CT) systems and methods are widely used,particularly for medical imaging and diagnosis. CT systems generallycreate images of one or more sectional slices through a subject's body.A radiation source, such as an X-ray tube, irradiates the body from oneside. The attenuation of the radiation that has passed through the bodyis measured by processing electrical signals received from the detector,which are then used to reconstruct an image of the body by performing aninverse Radon transformation (or an equivalent thereof).

Originally, energy-integrating detectors were used to measure CTprojection data, but more recently, photon-counting detectors havebecome a feasible alternative to conventional energy-integratingdetectors. Photon-counting detectors (PCDs) have many advantagesincluding being able to perform spectral CT. To obtain the spectralnature of the transmitted X-ray data, the photon-counting detectorssplit the X-ray beam into its component energies or spectrum bins andcount a number of photons in each of the bins. Since spectral CTinvolves the detection of transmitted X-rays at two or more energylevels, spectral CT generally includes dual-energy CT by definition.

Spectral and dual-energy CT are advantageous because they can be used toperform material decompositions, whereby bone can be distinguished fromsoft tissues in the body, providing more clinical information fordoctors and medical clinicians. Various configurations can be used forspectral imaging in CT. In general, the spectral CT configurationsbreakdown into two types, (i) generating different energy spectra atX-ray source in combination with energy integrating detector (e.g., fastkilo-voltage peak (kVp)-switching, and dual source configurations), and(ii) a broad-energy-spectrum X-ray source together with an energydiscriminating/resolving detector.

More particularly, there are four spectral CT configurations ofpractical significance: PCD spectrally resolving detectors, dual-layerdetectors, dual source and detector systems, and fast kVp-switching. Forexample, the PCDs discussed above can be used as energy resolvingdetectors with a broad-spectrum X-ray source. Another type of energyresolving detector uses various X-ray energy filters arranged in frontof respective energy integrating detectors, such that the filtersperform the function of separating the detected X-rays into differentenergy bands (e.g., a dual-layer detector that can separate photons byenergy). In a third spectral CT configuration, dual X-ray sources arearranged opposite respective detectors, each source-detector pairingforming its own CT scanner system without overlapping with orinterfering with the other (e.g., being arranged at right angles) andeach source-detector pairing operating at a different X-ray spectrumthan the other. With this arrangement, two CT scans with two differentX-ray spectra can be simultaneously performed. In a forth configuration,a single integrating source can be used with an X-ray that uses fastkVp-switching to rapidly alternate between a high-energy X-ray spectrumand low-energy X-ray spectrum as the view angle of the CT scannerrotates around the patient. However, each of these four alternatives forspectral/dual-energy CT has its own unique pitfalls and shortcomings.

For example, photon-counting detectors (PCDs) are susceptible to pulsepileup (i.e., multiple X-ray detection events occurring at a singledetector can within the detector's time response). Further, efforts tocurb pileup by making the PCDs smaller in cross-sectional area arelimited by tradeoffs due to increased susceptibility to chargemigration/sharing and k-escape.

In the dual-layer detector approach, the combination of scintillatorsand photo-multiplier tubes suffer from low-energy noise and from beingpoorly optimized to achieve out-of-band energy suppression. Further,energy separation is degraded by the significant overlap between the tworeadout spectra.

The dual-source and dual-detector configuration suffers from beingexpensive and from cross-scatter effects.

The fast kVp-switching configuration is also expensive due to theadditional costs associated with an ultra-high frequency generator andparallel data acquisitionsystems (DASs) used to acquire in parallel thehigh-energy and lose-energy projection data.

Accordingly, a better spectral CT approach is desired that overcomes theabove-identified deficiencies in the related approaches.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of this disclosure is provided byreference to the following detailed description when considered inconnection with the accompanying drawings, wherein:

FIG. 1 shows a schematic diagram of an arrangement of an X-ray sourceand detector in a computed tomography (CT) scanner, according to oneimplementation;

FIG. 2A shows an example of view angles/positions of an X-ray source forfast kVp-switching, according to one implementation;

FIG. 2B shows an example of kilo-voltage peak values as a function oftime for fast kVp-switching, according to one implementation;

FIG. 3A shows an example of view angles/positions of an X-ray source forsparse kVp-switching, according to one implementation;

FIG. 3B shows an example of kilo-voltage peak values as a function oftime for spars kVp-switching, according to one implementation;

FIG. 4 shows a plot of a probability density function for an energyspectra of X-rays emitted at low- and high-kVp switching, according toone implementation;

FIG. 5A shows an image reconstructed using an adaptive iterative dosereduction (AIDR) three-dimensional (3D) reconstruction method, the imagebeing reconstructed from sparse kVp-switching projection data for alow-kVp value of 80 kVp, according to one implementation;

FIG. 5B shows an image reconstructed using an AIDR-3D method, the imagebeing reconstructed from sparse kVp-switching projection data for ahigh-kVp value of 135 kVp, according to one implementation;

FIG. 5C shows a sum of the images in FIGS. 5A and 5B, according to oneimplementation;

FIG. 6 shows an example of a flow diagram for training and using a deeplearning (DL) artificial neural network (ANN) to correct artifacts insparse kVp-switching projection data, according to one implementation;

FIG. 7A shows an example of a high-quality image for full-viewprojection data acquired at 80 kVp to be paired with the image in FIG.5A in a training data set, according to one implementation;

FIG. 7B shows an example of a high-quality image for full-viewprojection data acquired at 135 kVp to be paired with the image in FIG.5B in a training data set, according to one implementation;

FIG. 8 shows an example of a two-channel DL-ANN network being trainedwith low- and high-quality image data to correct sparse-view artifactsarising from sparse kVp-switching, according to one implementation;

FIG. 9 shows an example of a partial flow diagram for training and usingthe DL-ANN to correct artifacts arising in images reconstructed fromsparse kVp-switching projection data, according to one implementation;

FIG. 10 shows an example of a flow diagram for training and using aDL-ANN to perform image-domain material decomposition, according to oneimplementation;

FIG. 11 shows an example of a two-channel DL-ANN network being trainedto perform the image-domain material decomposition, according to oneimplementation;

FIG. 12 shows a flow diagram in which two separate DL-ANN networks arerespectively trained and used to correct artifacts and performimage-domain material decomposition, according to one implementation;

FIG. 13 shows a flow diagram in which a single two-channel DL-ANNnetwork is trained and used to correct artifacts and performimage-domain material decomposition as an integrated process, accordingto one implementation;

FIG. 14 shows another flow diagram in which the single two-channelDL-ANN network is trained and used to correct artifacts and performimage-domain material decomposition as an integrated process, accordingto one implementation;

FIG. 15 shows the single two-channel DL-ANN network being trained toboth correct artifacts and perform image-domain material decompositionas an integrated process, according to one implementation;

FIG. 16 shows the single two-channel DL-ANN network being trained in thewhitening transform domain to both correct artifacts and performimage-domain material decomposition as an integrated process, accordingto one implementation;

FIG. 17 shows a flow diagram for iteratively adjusting coefficients of aDL-ANN network to optimize a loss-error function, and thereby trainingthe DL-ANN, according to one implementation;

FIG. 18A shows an example of a feedforward ANN, according to oneimplementation;

FIG. 18B shows an example of a type of ANN referred to as aconvolutional neural network (CNN), according to one implementation;

FIG. 19A shows an example of the 80 kVp image in FIG. 5A after it hasbeen applied to the artifact-reducing DL-ANN at step 230 of method 202,according to one implementation;

FIG. 19B shows an example of the 135 kVp image in FIG. 5B after it hasbeen applied to the artifact-reducing DL-ANN at step 230 of method 202),according to one implementation;

FIG. 20A shows an example of an image for an iodine material componentgenerated without using artifact reducing and material decompositionDL-ANN networks, according to one implementation;

FIG. 20B shows an example of an image for the iodine material componentgenerated using the artifact reducing and material decomposition DL-ANNnetworks, according to one implementation;

FIG. 20C shows an example of an image for a water material componentgenerated without using the artifact reducing and material decompositionDL-ANN networks, according to one implementation;

FIG. 20D shows an example of an image for the water material componentgenerated using the artifact reducing and material decomposition DL-ANNnetworks, according to one implementation;

FIG. 21 shows a first example of computed tomography (CT) scanner forsparse kv-switching, according to one implementation; and

FIG. 22 shows a second example of CT scanner for sparse kv-switching,according to one implementation.

DETAILED DESCRIPTION

The methods and apparatus described herein overcome the above-identifieddeficiencies of other spectral and dual-energy (DE) computed tomography(CT) approaches. These deficiencies are overcome by applyingreconstructed images from sparse-view projection data, which isgenerated using sparse kVp-switching, to a deep learning (DL) artificialneural network (ANN) to remove artifacts from and decompose the spectralreconstructed images into material component images.

As discussed above, related approaches to spectral CT suffer fromvarious deficiencies, including, e.g., higher costs and/or degradedimage quality.

For example, higher cost is a deficiency in both fast kVp-switching anddual/detector-source systems. The methods described herein usekVp-switching but, in contrast to fast kVp-switching systems, thekilo-voltage peak (kVp) applied across the X-ray tube of the source isswitched slowly (i.e., the kVp switching is sparse, resulting in sparseview projection data for both low- and high-kVp values). That is, ratherthan switching between low- and high-kVp values for each change of theprojection angle (view), the kVp-switching used herein is sparse,meaning the kVp-switching is performed less frequently, such that agiven kVp setting is maintained as the CT scanner rotates throughseveral projection angle before switching to the other kVp setting.Accordingly, after switching to a high kVp setting the X-ray sourcemaintains the high kVp voltage while the scanner rotates through andacquires projection images at many projection angles before switchingback to a low kVp setting, which is then maintained through the nextseveral projection angles, and so forth. In this way a single dataacquisition system (DAS) can be used, and simpler, less expensivehardware is sufficient to switch the voltage across the X-ray tube giventhe slower rate of kVp-switching.

The methods described herein avoid the deficiencies of photon countingdetectors (PCDs) such as pileup because the methods described herein useenergy integrating detectors, rather than PCDs. Further, the methodsdescribed herein overcome the deficiencies of dual-layer detectorsbecause the two energy spectra are achieved by modulating/switching thevoltage applied across the X-ray source, rather than by filtering theX-ray energies at the X-ray detector.

The methods described herein use sparse-kVp-switching dual-energy CT(DECT) system to generate sparse view projection data for a low- andhigh-energy X-ray spectra, respectively. Because the kVp-switching isperformed less frequently than in fast kVp-switching, the methodsdescribed herein can be performed using a high-frequency generator (asopposed to the ultra-high frequency generator used for fastkVp-switching). Further, the sparse kVp-switching can be performed usinga single sequential DAS (as opposed to the parallel DASs used for fastkVp-switching).

One of the major challenges for the the sparse kVp-switching approach toDECT and is that the sparse view data presents challenges withrespective to the image quality and material decomposition of thereconstructed images. More particularly, for sparse kVp-switchingprojection data, it has proven difficult to develop an efficientreconstruction algorithm that is not susceptible to streak artifacts,beam hardening, and other effects degrading the image quality. On theone hand, analytical reconstruction methods such as filteredback-projection (FBP) can be efficient, but, when they are applied tosparse projection data, they generate reconstructed images that sufferfrom streak artifacts. On the other hand, iterative reconstructionmethods, when applied to sparse projection data, can result in degradedspatial resolution and degraded noise texture due to the lower dose ofX-rays at each kVp setting. The “kVp” is the peak kilo-voltage (kV)applied between the anode and cathode of an X-ray tube source. Anotherchallenge with sparse kVp-switching projection data is that thetrajectories traced by the X-rays to the detector pixels are differentbetween the two kVp setting, rendering sinogram-domain materialdecomposition infeasible. However, image-domain material decompositionhas its own set of difficulties, including, e.g., beam hardeningcorrections and spatial variations in the energy spectrum of the X-raybeam (e.g., due to different paths through a bow tie filter).

To address the above challenges, the methods described herein use a deeplearning (DL) approach to mitigate artifacts in images reconstructedsparse-view projection data acquired using sparse kVp-switching DECTsystem. Further, the methods described herein use the deep learning (DL)approach also for image-domain material decomposition.

Referring now to the drawings, wherein like reference numerals designateidentical or corresponding parts throughout the several views, FIG. 1shows a configuration of an X-ray source and detector in computedtomography (CT) scanner having energy-integrating detectors arranged ina third-generation geometry. Illustrated in FIG. 1 is a non-limitingexample of relative positions among an imaged object OBJ resting on atable 116, an X-ray source 112, a collimator/filter 114, and a pixelatedX-ray detector 103, which includes an array of individual detectorelements (i.e., pixels).

In one implementation, the X-ray source 112, the collimator/filter 114are fixedly connected to a rotational component 110 that is rotatablyconnected to a gantry. For example, the rotational component 110 can bean annular ring configured to rotate within a gantry while the objectOBJ remains fixed in space on the table 116, or, alternatively, in ahelical scan the table can be translated along the bore of the gantrywhile the X-ray source 112 and the X-ray detector 103 are rotated aroundthe bore of the gantry. The gantry of the CT scanner also includes anopen aperture 115 within the bore, which can be centered at theiso-center of the rotational component 110. The open aperture 115enables the object OBJ to be placed in a projection plane of the X-raysfrom the X-ray source. In certain implementations, the X-ray detector103 is fixedly connected to another rotational component 130 that isrotatably connected to the gantry. In a rotate/rotate configuration, therotational component 110 and the rotational component 130 can rotate inunison, maintaining the X-ray detector 103 diametrical opposed to theX-ray source 112 to obtain projection data of the object OBJ at aprogression of projection angles (i.e., views). Sinograms are created byarranging the projection data with projection angles arranged along oneaxis and the spatial dimensions of the projection data arranged alongthe other axes. The projection data (sinograms) can be used toreconstruct a cross-sectional image of the object OBJ.

In spectral CT, projection data having multiple energy components isused to represent projective measurements of the object OBJ. Theseprojective measurements are made at a series of angles enablingconventional CT image reconstruction methods similar to non-spectral CT.However, unlike non-spectral CT, spectral CT generates additionalinformation (i.e., spectral attenuation information) enabling adecomposition of the projective measurements into material components.Setting aside k-edge methods, the number of materials is usually two,based on the unique spectral signatures of X-ray attenuation due toCompton scattering and photoelectric attenuation, respectively. That is,the spectral differences between the X-ray attenuation for two materialcomponents arise from the different ratios of Compton scattering tophotoelectric attenuation they exhibit e (e.g., the X-ray attenuationdue to a high-Z material like iodine is comprised of a different ratioof Compton scattering to photoelectric attenuation than a low-Z materiallike water).

Mapping the projection data from the spectral domain to the materialdomain can be performed either in the sinogram-domain (i.e., before theimage reconstruction process) or in the image-domain (i.e., after theimage reconstruction process). However, to be performed in the sinogram(projection) domain, the projection data should include identical (ornearly identical) X-ray trajectories for each of the dual-energycomponents. In fast kVp-switching, this achieved because the CT scannerhas rotated very little between adjacent high-kV and low-kV projectionviews. However, for sparse kVp-switching the difference between X-raytrajectories becomes large between the projection views in the middle ofa long sequence of images acquired at the same low- or high-kVp settingson the X-ray tube.

The attenuation of X-rays in biological materials is dominated by twophysical processes (i.e., photoelectric absorption and Comptonscattering). Thus, the attenuation coefficient as a function of energycan be approximated by the decompositionμ(E,x,y)=μ_(PE)(E,x,y)+μ_(c)(E,x,y),wherein μ(E, x, y) is the photoelectric attenuation and μ_(c)(E, x, y)is the Compton attenuation. Alternatively, this attenuation coefficientcan be rearranged into a decomposition of a high-Z material (i.e.,material 1) and a low-Z material (i.e., material 2) to becomeμ(E,x,y)=μ₁(E)c ₁(x,y)+μ₂(E)c ₂(x,y),wherein c, (x, y) and c₂ (x, y) are, respectively correspond to a firstand second material component. Material decomposition is the process ofsolving from the c, (x, y) and c₂ (x, y) that best approximate withmeasured/reconstructed attenuation spectra.

FIGS. 2A and 2B show diagrams of an implementation of fastkVp-switching. The kVp setting on the X-ray tube is changed for eachprojection angle. In FIG. 2A, the locations of the X-ray source foracquisitions of a projection image using a high-kVp (low-kVp) settingare indicated by the white (black) circles. In FIG. 2B, the voltageapplied to the X-ray tube is shown as a function of time, with the timeand voltage for projection images acquired at the high-kVp (low-kVp)setting indicated by the white (black) circles.

FIGS. 3A and 3B show diagrams of an implementation of sparsekVp-switching. The kVp setting on the X-ray tube is changed only after asuccession of N projection images at different projection angles havebeen acquired, where N is a number greater than one. In FIG. 3A, thelocations of the X-ray source for acquisitions of a projection imageusing a high-kVp (low-kVp) setting are indicated by the white (black)circles. In FIG. 3B, the voltage applied to the X-ray tube is shown as afunction of time, with the time and voltage for projection imagesacquired at the high-kVp (low-kVp) setting indicated by the white(black) circles.

In the non-limiting example of FIGS. 3A and 3B, N is three, but N can beany integer two or greater. Further, the number of successive projectionimages acquired at a given kVp setting does not need to be constantthroughout a CT scan, and different intervals can be applied betweendifferent kVp settings (e.g., within a given CT scan, more projectionimages can be acquired at a high-kVp setting than at a low-kVp settingor vice versa), as would be understood by a person of ordinary skill inthe art.

The method of sparse kVp-switching is illustrated herein using thenon-limiting example of a low-kVp setting of 80 kVp and a high-kVpsetting of 135 kVp. For an X-ray tube, the X-ray spectrum is mainlycontrolled by the voltage (kVp) applied between the anode and cathode toaccelerate the electrons before the electrons are suddenly stopped bycolliding with the cathode, converting the kinetic energy of theelectron into X-rays via a Bremsstrahlung radiation mechanism. By thisprocess, different X-ray spectra can be produced by changing the voltageapplied across of the X-ray tube. FIG. 4 shows the probabilitydistribution of the X-ray energy produced with a low-kVp setting of 80kVp and a high-kVp setting of 135 kVp, respectively.

As discussed above, a challenge of image reconstruction using projectiondata acquired using sparse kVp-switching is that the image quality tendsto be degraded due to streak artifacts. For example, FIGS. 5A and 5Bshow examples of reconstructed images generated using FBP reconstructionwith sparse kVp-switching projection data for 80 kVp and 135 kVp,respectively. The streak artifacts are clearly evident. Further, closeinspection reveals that the streak artifacts tend to be complementary.That is, a bright streak in the 80 kVp image corresponds to a darkstreak in the 135 kVp image, and vice versa. To illustrate this, FIG. 5Cshows a sum of the 80 kVp image with the 135 kVp image. To a significantdegree the bright streak artifacts in the 80 kVp image counteract thedark streak artifacts in the 135 kVp image. These streak artifacts canbe understood as arising from the fact that the projection angles forthe 80 kVp and 135 kVp projection data are complementary, as shown inFIG. 3A. For example, the sequences/intervals of projection angles forwhich the CT scanner is set to the High-kVp setting is the complement ofand is mutually exclusive with the sequences/intervals of projectionangles for which the CT scanner is set to the Low-kVp setting. Thus, theinformation from the two images can be combined to mitigate the streakartifacts.

The methods described herein use a DL-ANN to learn how to best to usethe combined information from the respective low- and high-kVpreconstructed images to correct for the streak artifacts to generatehigh-quality images. In certain implementations, reconstructing imagesusing an FBP reconstruction method and then applying the DL-ANN networkfor artifact and noise correction can be faster and yield comparable orbetter image quality than directly reconstructing a high-quality imageusing an iterative reconstruction method with an appropriately chosenregularizer (e.g., a total variation minimization (TV) regularizationterm).

FIG. 6 shows a flow diagram of a method 200 for sparse kVp-switching CTimage reconstruction that includes a process 310 of trainingartifact-correction network 351, and includes a process 202 of applyingthe trained artifact-correction network 351 to correct sparse viewreconstructed images 255 to ultimately generate high-qualitymaterial-basis images 257.

In process 310, a loss function is used to iteratively adjust parameters(e.g., weights and biases of convolutional and pooling layers) of theDL-ANN network 351 until stopping criteria are satisfied (e.g.,convergence of the network coefficients/parameters to a predefinedthreshold) to generate the trained network 351. The loss functioncompares high-quality training data 353 (e.g., full-scan images at therespective kVp settings) to a result arising from applying a low-qualitytraining data 355 (e.g., sparse kVp-switching images) to a currentversion of the DL-ANN network 351. Each pair of high-quality images(e.g., one high-quality image acquired at each of the two kVp settings)is combined with a corresponding pair of low-quality images generated byimaging the same object OBJ or phantom as imaged in the pair ofhigh-quality images. The high-quality images can be referred to as the“target” or “label” and the low-quality images are referred to as the“input.” The training data can include a large set of correspondingtargets and inputs. The offline training can be performed in advance ofa given CT scan (e.g., when a CT scanner is initially installed andcalibrated), and the trained DL-ANN network 351 can be stored in memoryuntil a CT scan is acquired and the image reconstruction is performed instep 210.

FIGS. 7A and 7B show examples of high-quality images generated at kVpsettings of 80 kV and 135 kV, respectively. The high-quality images canbe reconstructed using a full-view scan at each kVp setting, rather thanusing sparse-view projection data. Further, the high-quality images canbe reconstructed using techniques that are known to produce better imagequality (e.g., iterative reconstruction with regularization anddenoising) The images in FIGS. 7A and 7B could be used as the targetimages in combination with the images in FIGS. 5A and 5B, which would beused as input images, to form part of a training data set that would beused to train a network 351.

FIG. 8 shows an example of this, in which the DL-ANN network 351 intrained using two input images (i.e., a low-kV input image 355(L), whichis similar to the image in FIG. 5A, and a high-kV input image 355(H),which is similar to the image in FIG. 5B) and two target images (i.e., alow-kV target image 353(L), which is similar to the image in FIG. 7A,and a high-kV target image 353(H), which is similar to the image in FIG.7B). Further, the DL-ANN network 351 is a two-channel based network thattakes two input images 355(L) and 355(H) and generates two results thatare compared to the two target images 353(L) and 353(H) via the lossfunction.

After generating the trained network 351, step 230 of process 202 isused to apply the trained network 351 to generate a high-qualityspectral-CT image 253 from the low-quality spectral-CT image 255 arisingfrom step 220 of process 210, as shown in FIG. 9.

Returning to FIG. 6, in process 202, CT projection data 251 that havebeen generated using sparse kVp-switching are processed using steps 210,220, 230 and 240 to generate high-quality material images.

In certain implementations, the CT projection data 251 can be projectiondata acquired from a CT scan that are pre-processed at step 210 (e.g.,signal preconditioning, calibration corrections, baseline corrections,beam hardening corrections, etc.). The projection data 251 can be asinogram that is corrected using various calibration and geometricfactors. For example, the pre-processing can include corrections for adetector offset and gain, variations in quantum efficiency in thedetectors, etc. Further, these corrections can be based on calibrationdata, empirical derived parameters, and a priori known parameters.

In step 220 of process 202, the image reconstruction can be performedusing a back-projection method, a filtered back-projection method, aFourier-transform-based image reconstruction method, an iterative imagereconstruction method, a matrix-inversion image reconstruction method, astatistical image reconstruction method, or other reconstruction methodas would be understood as a person of ordinary skill in the art. Forexample, the reconstruction method can use a helical reconstructiontechnique, a cone-beam reconstruction technique, a Feldkamp algorithm, aFBP reconstruction method, and an adaptive iterative dose reduction(AIDR) three-dimensional (3D) reconstruction method with noise reductionin one or both of the image and sinogram domains. The reconstruction caninclude denoising, corrections to minimize photon starvation in highattenuation regions, corrections to mitigate quantum noise,edge-preservation/enhancement corrections, and filtering (e.g., linearand non-linear filtering and denoising).

In certain implementations, rreconstruction of sparse-view data at eachkV respectively results in artifacts due to the missing samples at someview angles. Since low- and high-energy data are complementary to eachother, using a two-channel network that analyses the mutual informationprovides significant benefit over a one-channel network that considerseach energy component (kVp setting) separately.

Accordingly, in step 230 of process 202, the network 351 can be atwo-channel network that utilizes the complementary information betweenthe low- and high-energy projection data. In certain implementations,the training performed in step 312 uses input images that are generatedusing the same reconstruction method as in step 220. For example, step220 can use a FBP based method such as the AIDR 3D method, and the inputimages in step 312 can be AIDR 3D reconstruction images acquired usingsparse kVp-switching with high- and low-kVp values respectively. Sincethe acquired projection data at each kVp setting is sparse, theresulting image suffers sparse-sampling artifacts. The target images canbe the AIDR 3D reconstruction images acquired using projection data froma full-view rotate-rotate dual-energy scan. Since the sampling iscomplete at each kV for these target images, the target images areartifacts-free. Further the target images can be reconstructed using anyother reconstruction method that generates high-quality images with lownoise (e.g., an iterative reconstruction method with an edge-preservingor TV regularizer). Thus, in certain implementations of step 230, whenthe low-quality images 255(H) and 255(L) are applied to the two-channelnetwork 351, the generated high-quality images 253(H) and 253(L) canexhibit both reduced artifact and reduced noise.

In step 240 of process 202, material decomposition can be performed onthe images 253(H) and 253(L) to generate material-component images 257.In general, any material decomposition method can be used to generatematerial-component images 257. FIGS. 10-12 show an implementation ofmethod 200 in which step 240 is performed using a DL-ANN network 361 toperform the material decomposition.

As discussed above, in general, the material decomposition mapping fromthe spectral domain to the material-component domain can be performedeither before or after the image reconstruction process. However, insparse kVp-switching material decomposition is simpler to perform in theimage domain than in the projection (sinogram) domain. As discussedabove, to perform material decomposition in the projection (sinogram)domain, the projection data should include identical (or nearlyidentical) X-ray trajectories for each of the measured energycomponents. In fast kVp-switching, this achieved because the CT scannerhas rotated very little between adjacent high-kV and low-kV. However,for sparse kVp-switching the differences can become large between theprojection angles for high- and low-kV and low-kVp settings, makingprojection-domain material decomposition challenging.

The DL-ANN network 361 can be particular effective at addressing thechallenges of image-domain material decomposition in dual-energy CT(DECT). There are two challenges for conventional image-spacedecomposition. First, the X-ray spectrum of the X-ray beam can bespatially varying due to bowtie filtration. Therefore, the image-spacematerial decomposition matrix is ideally also spatially varying. Second,correcting for beam-hardening artifacts is straightforward and efficientin sinogram-domain material decomposition because the contributions ofeach material are known along the X-ray trajectories. In contrast, forimage-space material decomposition, the beam-hardening correction ismore complicated and typically less efficient, requiring time consuming,iterative corrections. Using the DL method described herein, however,image-space material decomposition with beam-hardening corrections canbe performed more efficiently than in previous methods. For example, toaddress the above challenges, deep learning is used with a two-channelnetwork 361 to quickly perform material decomposition while correctingfor beam hardening and for spatial variations in the spectrum of theX-ray beam.

To realize these improvements, FIGS. 10 and 11 shows that the trainingdata includes that the input images are the two high-quality kVp images353(H) and 353(L), and that the target images are a basis-material imagefor a first material component 363(1) and a basis-material image for asecond material component 363(2). The material-component images 363(1)and 363(2) can be obtained by performing sinogram-domain materialdecomposition on full-scan projection data and then reconstructinghigh-quality material images 363(1) and 363(2) from material componentprojection data (i.e., material decomposition is performed in thesinogram domain prior to reconstruction). Thus, the material-componentimages 363(1) and 363(2) due not suffer from inaccurate beam-hardeningcorrections and spatial-spectrum corrections for the X-ray beam becausespatially variations in the spectrum and beam hardening corrections canbe fully modeled in the sinogram domain. Accordingly, by training on thematerial-component images 363(1) and 363(2), the DL-ANN network 361 canbe trained to more accurately and efficiently perform image-domainmaterial decomposition than is achieved in related image-domain methods.

In FIG. 12, a flow diagram of method 200 is provided of animplementation that includes both the artifact reduction DL-ANN network351 and the image-domain material decomposition DL-ANN network 361.

FIGS. 13 and 14 shows flow diagrams of method 200′, which is a variationof method 200 in which the artifact reduction and material decompositionof step 230 and 240 have been combined into a single step 250. TheDL-ANN network 371 combines both functions of artifact reduction andmaterial decomposition, which are integrated into a single networkbecause, as shown in FIG. 15. The DL-ANN network 371 is trained in step332 using the low-quality images 355(H) and 355(L) as inputs and thehigh-quality material images 363(1) and 363(2) as targets.

In certain implementations, in training step 332, the input images tothe DL-ANN network 371 are the high- and low-kVp images that have beenreconstructed using, e.g., an AIDR 3D or a FBP reconstruction method.Since the images are reconstructed from incomplete sampling data, theinput images are subject to sparse-sampling artifacts. The target imagesof the DL-ANN network 371 are the high-quality basis-material imagesfrom a full-view rotate-rotate dual-energy CT scan, as shown in FIG. 15.The DL-ANN network 371 can use a two-channel DL-ANN network structure

FIG. 16 shows a diagram of a step 332′, which is a variation of step332′. In step 332″, the target images are whitening-transformed images363′(1) and 363′(2) generated by performing a whitening transform on thehigh-quality material images 363(1) and 363(2). That is, thematerial-component images 363(1) and 363(2) are related to thewhitening-transformed images 363′(1) and 363′(2) by a whiteningtransform that de-correlates noise. Thus, when the network 371′ isapplied to the low-quality images 255 in step 250 of process 202′, theresult will be whitening-transformed images 257′(1) and 257′(2), and, instep 250, an inverse whitening is performed on the whitening-transformedimages 257′(1) and 257′(2) to generate the material-component images257(1) and 257(2).

To optimize the mono-energetic image quality at clinically relevantdiagnostic energy range, whitening transform can be applied to the deeplearning target image. When the whitening transform matrix is estimatedfrom the 75 keV linear attenuate coefficients, the deep learning tendsto optimize the network coefficients to provide high-quality 75 keVmono-energetic image. The basis material decomposition can introducecorrelated noise to the basis material image. The whitening transformcan de-correlate the noise in two basis material images. Therefore, thetrained network can efficiently reduce noise in learning process. Inreconstruction process 202, the high-quality material-basis images257(1) and 257(2) can be obtained by performing an inverse whiteningtransformation on the output images 257′(1) and 257′(2) from the DL-ANNnetwork 371′, when applied in step 250.

Now a more detailed description of training a DL-ANN network isprovided. This description is illustrated using step 312 as an exampleof training a network, but also applies to the training in performedsteps 322, 332, and 332′, as would be understood by a person of ordinaryskill in the art. FIG. 17 shows a flow diagram of one implementation ofthe training step 312. In step 312, low-quality (e.g., noisy) images 355and high-quality (e.g., optimized) images 353 are used as training datato train a DL-ANN network, resulting in the DL-ANN network being outputfrom step 318. In general, the images 355 can be any defect-exhibitingimages or input images, for which the “defect” can be any undesirablecharacteristic that can be affected through image processing (e.g.,noise or an artifact). Similarly, images 353 can be referred to astarget data, defect-reduced data, defect-minimized data, or optimizeddata, for which the “defect” is less than in the images 355. The offlineDL training step 312 trains the DL-ANN network 351 using a large numberof input images 355 that are paired with corresponding target images 353to train the DL-ANN network 351 to produce images resembling the targetimages 353 from the input images 355.

In step 312, a set of training data is obtained, and the network 351 isiteratively updated to reduce the error (e.g., the value produced by aloss function). In other words, DL-ANN network infers the mappingimplied by the training data, and the cost function produces an errorvalue related to the mismatch between the target images 353 and theresult produced by applying a current incarnation of the DL-ANN network351 to the input images 355. For example, in certain implementations,the cost function can use the mean-squared error to minimize the averagesquared error. In the case of a of multilayer perceptrons (MLP) neuralnetwork, the backpropagation algorithm can be used for training thenetwork by minimizing the mean-squared-error-based cost function using a(stochastic) gradient descent method.

In step 313 of step 312, an initial guess is generated for thecoefficients of the DL-ANN network 351. For example, the initial guesscan be based on a priori knowledge of the region being imaged or one ormore exemplary denoising methods, edge-detection methods, and/or blobdetection methods. Additionally, the initial guess can be based on oneof a LeCun initialization, an Xavier initialization, and a Kaiminginitialization.

Steps 314 through 318 provide a non-limiting example of an optimizationmethod for training the DL-ANN network 351.

In step 314, an error is calculated (e.g., using a loss function or acost function) to represent a measure of the difference (e.g., adistance measure) between the target images 353 (i.e., ground truth) andinput images 355 after applying a current version of the network 351.The error can be calculated using any known cost function or distancemeasure between the image data, including those cost functions describedabove. Further, in certain implementations the error/loss function canbe calculated using one or more of a hinge loss and a cross-entropyloss.

Additionally, the loss function can be combined with a regularizationapproach to avoid overfitting the network to the particular instancesrepresented in the training data. Regularization can help to preventoverfitting in machine learning problems. If trained too long, andassuming the model has enough representational power, the network willlearn the noise specific to that dataset, which is referred to asoverfitting. In case of overfitting, the DL-ANN becomes a poorgeneralization, and the variance will be large because the noise variesbetween datasets. The minimum total error occurs when the sum of biasand variance are minimal. Accordingly, it is desirable to reach a localminimum that explains the data in the simplest possible way to maximizethe likelihood that the trained network represents a general solution,rather than a solution particular to the noise in the training data.This goal can be achieved, e.g., by early stopping, weightregularization, lasso regularization, ridge regularization, or elasticnet regularization.

In certain implementations, the network 351 is trained usingbackpropagation. Backpropagation can be used for training neuralnetworks and is used in conjunction with gradient descent optimizationmethods. During a forward pass, the algorithm computes the network'spredictions based on the current parameters Θ. These predictions arethen input into the loss function, by which they are compared to thecorresponding ground truth labels (i.e., the high-quality image 353).During the backward pass, the model computes the gradient of the lossfunction with respect to the current parameters, after which theparameters are updated by taking a step of size of a predefined size inthe direction of minimized loss (e.g., in accelerated methods, such thatthe Nesterov momentum method and various adaptive methods, the step sizecan be selected to more quickly converge to optimize the loss function).

The optimization method by which the back projection is performed canuse one or more of gradient descent, batch gradient descent, stochasticgradient descent, and mini-batch stochastic gradient descent.Additionally, the optimization method can be accelerated using one ormore momentum update techniques in the optimization approach thatresults in faster convergence rates of stochastic gradient descent indeep networks, including, e.g, Nesterov momentum technique or anadaptive method, such as Adagrad sub-gradient method, an Adadelta orRMSProp parameter update variation of the Adagrad method, and an Adamadaptive optimization technique. The optimization method can also applya second order method by incorporating the Jacobian matrix into theupdate step.

The forward and backwards passes can be performed incrementally throughthe respective layers of the network. In the forward pass, the executionstarts by feeding the inputs through the first layer, thus creating theoutput activations for the subsequent layer. This process is repeateduntil the loss function at the last layer is reached. During thebackward pass, the last layer computes the gradients with respect to itsown learnable parameters (if any) and also with respect to its owninput, which serves as the upstream derivatives for the previous layer.This process is repeated until the input layer is reached.

Returning to the non-limiting example shown in FIG. 17, step 315determines a change in the error as a function of the change in thenetwork can be calculated (e.g., an error gradient), and this change inthe error can be used to select a direction and step size for asubsequent change to the weights/coefficients of the DL-ANN network 351.Calculating the gradient of the error in this manner is consistent withcertain implementations of a gradient descent optimization method. Incertain other implementations, this step can be omitted and/orsubstituted with another step in accordance with another optimizationalgorithm (e.g., a non-gradient descent optimization algorithm likesimulated annealing or a genetic algorithm), as would be understood byone of ordinary skill in the art.

In step 316, a new set of coefficients are determined for the DL-ANNnetwork 351. For example, the weights/coefficients can be updated usingthe changed calculated in step 315, as in a gradient descentoptimization method or an over-relaxation acceleration method.

In step 317, a new error value is calculated using the updatedweights/coefficients of the DL-ANN network 351.

In step 318, predefined stopping criteria are used to determine whetherthe training of the network is complete. For example, the predefinedstopping criteria can evaluate whether the new error and/or the totalnumber of iterations performed exceed predefined values. For example,the stopping criteria can be satisfied if either the new error fallsbelow a predefined threshold or if a maximum number of iterations isreached. When the stopping criteria is not satisfied the trainingprocess performed in step 312 will continue back to the start of theiterative loop by returning and repeating step 315 using the new weightsand coefficients (the iterative loop includes steps 315, 316, 317, and318). When the stopping criteria are satisfied the training processperformed in step 312 is completed.

FIGS. 18A and 18B show various examples of the inter-connections betweenlayers in the DL-ANN network 351. The DL-ANN network 351 can includefully connected, convolutional, and the pooling layer, all of which areexplained below. In certain preferred implementations of the DL-ANNnetwork 351, convolutional layers are placed close to the input layer,whereas fully connected layers, which perform the high-level reasoning,are place further down the architecture towards the loss function.Pooling layers can be inserted after convolutions and proved a reductionlowering the spatial extent of the filters, and thus the amount oflearnable parameters. Activation functions are also incorporated intovarious layers to introduce nonlinearity and enable the network to learncomplex predictive relationships. The activation function can be asaturating activation functions (e.g., a sigmoid or hyperbolic tangentactivation function) or rectified activation function (e.g., theRectified Linear Unit (ReLU) applied in the first and second examplesdiscussed above). The layers of the DL-ANN network 351 can alsoincorporate batch normalization, as also exemplified in the first andsecond examples discussed above.

FIG. 18A shows an example of a general artificial neural network (ANN)having N inputs, K hidden layers, and three outputs. Each layer is madeup of nodes (also called neurons), and each node performs a weighted sumof the inputs and compares the result of the weighted sum to a thresholdto generate an output. ANNs make up a class of functions for which themembers of the class are obtained by varying thresholds, connectionweights, or specifics of the architecture such as the number of nodesand/or their connectivity. The nodes in an ANN can be referred to asneurons (or as neuronal nodes), and the neurons can haveinter-connections between the different layers of the ANN system. Thesimplest ANN has three layers, and is called an autoencoder. The DL-ANNnetwork 351 can have more than three layers of neurons, and has as manyoutputs neurons as input neurons, wherein N is the number of pixels inthe reconstructed image. The synapses (i.e., the connections betweenneurons) store values called “weights” (also interchangeably referred toas “coefficients” or “weighting coefficients”) that manipulate the datain the calculations. The outputs of the ANN depend on three types ofparameters: (i) the interconnection pattern between the different layersof neurons, (ii) the learning process for updating the weights of theinterconnections, and (iii) the activation function that converts aneuron's weighted input to its output activation.

Mathematically, a neuron's network function m (x) is defined as acomposition of other functions n_(i)(x), which can further be defined asa composition of other functions. This can be conveniently representedas a network structure, with arrows depicting the dependencies betweenvariables, as shown in FIG. 18. For example, the ANN can use a nonlinearweighted sum, wherein m(x)=K(Σ_(i)w_(i)n_(i)(x)), where K (commonlyreferred to as the activation function) is some predefined function,such as the hyperbolic tangent.

In FIG. 18A (and similarly in FIG. 18B), the neurons (i.e., nodes) aredepicted by circles around a threshold function. For the non-limitingexample shown in FIG. 18A, the inputs are depicted as circles around alinear function, and the arrows indicate directed connections betweenneurons. In certain implementations, the DL-ANN network 351 is afeedforward network.

FIG. 18B shows a non-limiting example in which the DL-ANN network 351 isa convolutional neural network (CNN). CNNs are type of ANN that hasbeneficial properties for image processing, and, therefore, havespecially relevancy for the applications of image denoising. CNNs usefeed-forward ANNs in which the connectivity pattern between neurons canrepresent convolutions in image processing. For example, CNNs can beused for image-processing optimization by using multiple layers of smallneuron collections which process portions of the input image, calledreceptive fields. The outputs of these collections can then tiled sothat they overlap, to obtain a better representation of the originalimage. This processing pattern can be repeated over multiple layershaving alternating convolution and pooling layers.

Following after a convolutional layer, a CNN can include local and/orglobal pooling layers, which combine the outputs of neuron clusters inthe convolution layers. Additionally, in certain implementations, theCNN can also include various combinations of convolutional and fullyconnected layers, with pointwise nonlinearity applied at the end of orafter each layer.

CNNs have several advantages for image processing. To reduce the numberof free parameters and improve generalization, a convolution operationon small regions of input is introduced. One significant advantage ofcertain implementations of CNNs is the use of shared weight inconvolutional layers, which means that the same filter (weights bank) isused as the coefficients for each pixel in the layer; this both reducesmemory footprint and improves performance. Compared to otherimage-processing methods, CNNs advantageously use relatively littlepre-processing. This means that the network is responsible for learningthe filters that in traditional algorithms were hand-engineered. Thelack of dependence on prior knowledge and human effort in designingfeatures is a major advantage for CNNs.

FIGS. 19A and 19B respectively show examples of high-quality low- andhigh-kVp images generated by step 230 from the low-quality low- andhigh-kVp images in FIGS. 5A and 5B. As can be seen by comparing the low-and high-quality images, processing using the network 351 produces asignificant improvement in image quality.

FIGS. 20A and 20C respectively show iodine and water material componentimages generated using conventional material decomposition methods onthe low-quality low- and high-kVp images in FIGS. 5A and 5B. FIGS. 20Band 20D respectively show iodine and water material component imagesgenerated using applying the high-quality images in FIGS. 19A and 19B tothe DL-ANN network 361 in step 240. A comparison of the iodine images inFIGS. 20A and 20B (and similarly a comparison of the water images inFIGS. 20C and 20D) reveals that method 200 produces a significantimprovement in image quality for images reconstructed from sparsekVp-switching projection data.

FIG. 21 shows a first implementation of a computed tomography (CT)scanner having energy-integrating detectors arranged in athird-generation geometry. The diagram illustrates relative positionsamong the X-ray source 112, the collimator/filter 114, the X-raydetector 103, and the photon-counting detectors PCD1 through PCDN.

In addition to the configuration of the X-ray source 112 and thedetector unit 103 shown in FIG. 21, other types and combinations ofX-ray detectors and X-ray source can be used to obtain the projectiondata.

FIG. 21 also shows circuitry and hardware for acquiring, storing,processing, and distributing X-ray projection data. The circuitry andhardware include: a processor 170, a memory 178, and a data acquisitionsystem 176.

As the X-ray source 112 and the detector unit 103 are housed in a gantry140 and rotate around circular path of the rotational component 110. Thedetector elements in the detector unit 103 detect the X-ray radiationthat has been transmitted and output the detected signals as thedetector unit 103 rotates. In one implementation, the detector unit 103has densely placed energy-integrating detectors in predetermined channeland segment directions on the detector unit surface.

In one implementation, the X-ray source 112 is optionally a single X-raysource that is configured to perform a kVp-switching function foremitting X-ray radiation at a predetermined high-level energy and at apredetermined low-level energy.

The detector unit 103 can use energy integrating detectors such asscintillation elements with photo-multiplier tubes or avalanchephoto-diodes to detect the resultant scintillation photons fromscintillation events resulting from the X-ray radiation interacting withthe scintillator elements. The scintillator elements can be crystalline,an organic liquid, a plastic, or other know scintillator.

The CT scanner also includes a data channel that routes projectionmeasurement results from the photon-counting detectors and the detectorunit 103 to a data acquisition system 176, a processor 170, and memory178. The data acquisition system 176 controls the acquisition,digitization, and routing of projection data from the detectors. Thedata acquisition system 176 also includes radiography control circuitryto control the rotation of the annular rotating frames 110 and 130. Inone implementation data acquisition system 176 will also control themovement of the bed 116, the operation of the X-ray source 112, and theoperation of the X-ray detectors 103. The data acquisition system 176can be a centralized system or alternatively it can be a distributedsystem. In an implementation, the data acquisition system 176 isintegrated with the processor 170. The processor 170 performs functionsincluding reconstructing images from the projection data,pre-reconstruction processing of the projection data, andpost-reconstruction processing of the image data. The processor 170 alsoperforms the functions and methods described herein.

The pre-reconstruction processing of the projection data can includecorrecting for detector calibrations, detector nonlinearities, polareffects, noise balancing, and material decomposition. Additionally, thepre-reconstruction processing can include various processing in step210.

Post-reconstruction processing can include filtering and smoothing theimage, volume rendering processing, and image difference processing asneeded. Additionally, the Post-reconstruction processing can includesteps from the various implementations method 200, including process202, 310, 320, and 330.

The image-reconstruction process can be performed using filteredback-projection, iterative-image-reconstruction methods, orstochastic-image-reconstruction methods. Additionally, theimage-reconstruction processing can include step 220.

Both the processor 170 and the data acquisition system 176 can make useof the memory 176 to store, e.g., projection data, reconstructed images,calibration data and parameters, and computer programs.

The processor 170 can include a CPU and a network controller. The CPUcan be implemented as discrete logic gates, as an Application SpecificIntegrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) orother Complex Programmable Logic Device (CPLD). An FPGA or CPLDimplementation may be coded in VHDL, Verilog, or any other hardwaredescription language and the code may be stored in an electronic memorydirectly within the FPGA or CPLD, or as a separate electronic memory.Further, the memory may be non-volatile, such as ROM, EPROM, EEPROM orFLASH memory. The memory can also be volatile, such as static or dynamicRAM, and a processor, such as a microcontroller or microprocessor, maybe provided to manage the electronic memory as well as the interactionbetween the FPGA or CPLD and the memory.

Alternatively, the CPU in the reconstruction processor may execute acomputer program including a set of computer-readable instructions thatperform the functions described herein, the program being stored in anyof the above-described non-transitory electronic memories and/or a harddisk drive, CD, DVD, FLASH drive or any other known storage media.Further, the computer-readable instructions may be provided as a utilityapplication, background daemon, or component of an operating system, orcombination thereof, executing in conjunction with a processor, such asa Xenon processor from Intel of America or an Opteron processor from AMDof America and an operating system, such as Microsoft VISTA, UNIX,Solaris, LINUX, Apple, MAC-OS and other operating systems known to thoseskilled in the art. Further, CPU can be implemented as multipleprocessors cooperatively working in parallel to perform theinstructions.

In one implementation, the reconstructed images can be displayed on adisplay. The display can be an LCD display, CRT display, plasma display,OLED, LED or any other display known in the art. The network controllercan be, e.g., an Intel Ethernet PRO network interface card from IntelCorporation of America, can interface between the various parts of theCT scanner. Additionally, the network controller can also interface withan external network. As can be appreciated, the external network can bea public network, such as the Internet, or a private network such as anLAN or WAN network, or any combination thereof and can also include PSTNor ISDN sub-networks. The external network can also be wired, such as anEthernet network, or can be wireless such as a cellular networkincluding EDGE, 3G and 4G wireless cellular systems. The wirelessnetwork can also be WiFi, Bluetooth, or any other wireless form ofcommunication that is known.

The memory 178 can be a hard disk drive, CD-ROM drive, DVD drive, FLASHdrive, RAM, ROM or any other electronic storage known in the art.

FIG. 22 illustrates a second implementation of the radiography gantryincluded in a CT apparatus or scanner 100. As shown in FIG. 22, aradiography gantry 1000 is illustrated from a side view and furtherincludes an X-ray tube 1001, an annular frame 1002, and a multi-row ortwo-dimensional-array-type X-ray detector 1003. The X-ray tube 1001 andX-ray detector 1003 are diametrically mounted across an object OBJ onthe annular frame 1002, which is rotatably supported around a rotationaxis RA. A rotating unit 1007 rotates the annular frame 1002 at a highspeed, such as 0.4 sec/rotation, while the object OBJ is being movedalong the axis RA into or out of the illustrated page.

The first embodiment of an X-ray computed tomography (CT) apparatusaccording to the present inventions will be described below withreference to the views of the accompanying drawing. Note that X-ray CTapparatuses include various types of apparatuses, e.g., arotate/rotate-type apparatus in which an X-ray tube and X-ray detectorrotate together around an object to be examined, and astationary/rotate-type apparatus in which many detection elements arearrayed in the form of a ring or plane, and only an X-ray tube rotatesaround an object to be examined. The present inventions can be appliedto either type. In this case, the rotate/rotate type, will beexemplified.

The multi-slice X-ray CT apparatus further includes a high voltagegenerator 1009 that generates a tube voltage applied to the X-ray tube1001 through a slip ring 1008 so that the X-ray tube 1001 generatesX-rays. The X-rays are emitted towards the object OBJ, whose crosssectional area is represented by a circle. For example, the X-ray tube1001 having an average X-ray energy during a first scan that is lessthan an average X-ray energy during a second scan. Thus, two or morescans can be obtained corresponding to different X-ray energies. TheX-ray detector 1003 is located at an opposite side from the X-ray tube1001 across the object OBJ for detecting the emitted X-rays that havetransmitted through the object OBJ. The X-ray detector 1003 furtherincludes individual detector elements or units.

The CT apparatus further includes other devices for processing thedetected signals from X-ray detector 1003. A data acquisition circuit ora Data Acquisition System (DAS) 1004 converts a signal output from theX-ray detector 1003 for each channel into a voltage signal, amplifiesthe signal, and further converts the signal into a digital signal. TheX-ray detector 1003 and the DAS 1004 are configured to handle apredetermined total number of projections per rotation (TPPR).

The above-described data is sent to a preprocessing circuitry 1006,which is housed in a console outside the radiography gantry 1000 througha non-contact data transmitter 1005. The preprocessing circuitry 1006performs certain corrections, such as sensitivity correction on the rawdata. A storage 1012 stores the resultant data, which is also calledprojection data at a stage immediately before reconstruction processing.The storage 1012 is connected to a processing circuitry 1010 through adata/control bus 1011, together with a reconstruction device 1014, inputinterface 1015, and display 1016. The processing circuitry 1010 controlsa current regulator 1013 that limits the current to a level sufficientfor driving the CT system.

The detectors are rotated and/or fixed with respect to the patient amongvarious generations of the CT scanner systems. In one implementation,the above-described CT system can be an example of a combinedthird-generation geometry and fourth-generation geometry system. In thethird-generation system, the X-ray tube 1001 and the X-ray detector 1003are diametrically mounted on the annular frame 1002 and are rotatedaround the object OBJ as the annular frame 1002 is rotated about therotation axis RA. In the fourth-generation geometry system, thedetectors are fixedly placed around the patient and an X-ray tuberotates around the patient. In an alternative embodiment, theradiography gantry 1000 has multiple detectors arranged on the annularframe 1002, which is supported by a C-arm and a stand.

The storage 1012 can store the measurement value representative of theirradiance of the X-rays at the X-ray detector unit 1003. Further, thestorage 1012 can store a dedicated program for executing the methodsdescribed herein (e.g., method 200 and variations thereof).

The reconstruction circuitry 1014 can execute various steps of methodsdescribed herein (e.g., step 220 of method 200 and variations thereof).Further, reconstruction circuitry 1014 can execute pre-reconstructionprocessing image processing such as volume rendering processing andimage difference processing as needed.

The pre-reconstruction processing of the projection data performed bythe preprocessing circuitry 1006 can include correcting for detectorcalibrations, detector nonlinearities, and polar effects, for example.Further, the pre-reconstruction processing can include step 210.

Post-reconstruction processing performed by the reconstruction circuitry1014 can include filtering and smoothing the image, volume renderingprocessing, and image difference processing as needed. The imagereconstruction process can implement various steps of method 200 (e.g.,steps 230, 230, and 250) and also the offline training of the DL-ANNnetworks (e.g., process 310, 320, and 330). The reconstruction circuitry1014 can use the memory to store, e.g., projection data, reconstructedimages, calibration data and parameters, and computer programs.

The reconstruction circuitry 1014 can include a CPU (processingcircuitry) that can be implemented as discrete logic gates, as anApplication Specific Integrated Circuit (ASIC), a Field ProgrammableGate Array (FPGA) or other Complex Programmable Logic Device (CPLD). AnFPGA or CPLD implementation may be coded in VHDL, Verilog, or any otherhardware description language and the code may be stored in anelectronic memory directly within the FPGA or CPLD, or as a separateelectronic memory. Further, the storage 1012 can be non-volatile, suchas ROM, EPROM, EEPROM or FLASH memory. The storage 1012 can also bevolatile, such as static or dynamic RAM, and a processor, such as amicrocontroller or microprocessor, can be provided to manage theelectronic memory as well as the interaction between the FPGA or CPLDand the memory.

Alternatively, the CPU in the reconstruction circuitry 1014 can executea computer program including a set of computer-readable instructionsthat perform the functions described herein, the program being stored inany of the above-described non-transitory electronic memories and/or ahard disk drive, CD, DVD, FLASH drive or any other known storage media.Further, the computer-readable instructions may be provided as a utilityapplication, background daemon, or component of an operating system, orcombination thereof, executing in conjunction with a processor, such asa Xenon processor from Intel of America or an Opteron processor from AMDof America and an operating system, such as Microsoft VISTA, UNIX,Solaris, LINUX, Apple, MAC-OS and other operating systems known to thoseskilled in the art. Further, CPU can be implemented as multipleprocessors cooperatively working in parallel to perform theinstructions.

In one implementation, the reconstructed images can be displayed on adisplay 1016. The display 1016 can be an LCD display, CRT display,plasma display, OLED, LED or any other display known in the art.

The storage 1012 can be a hard disk drive, CD-ROM drive, DVD drive,FLASH drive, RAM, ROM or any other electronic storage known in the art.

While certain implementations have been described, these implementationshave been presented by way of example only, and are not intended tolimit the teachings of this disclosure. Indeed, the novel methods,apparatuses and systems described herein may be embodied in a variety ofother forms; furthermore, various omissions, substitutions and changesin the form of the methods, apparatuses and systems described herein maybe made without departing from the spirit of this disclosure.

The invention claimed is:
 1. An apparatus, comprising: processingcircuitry configured to obtain projection data representing an intensityof X-ray radiation at a plurality of detector elements, the projectiondata representing first sparse-view data acquired using a first voltageapplied to an X-ray source and second sparse-view data acquired using asecond voltage applied to the X-ray source, the second voltage beinggreater than the first voltage and the first sparse-view data beingacquired at views that are different from views at which the secondsparse-view data is acquired, reconstruct, from the first sparse-viewdata, a first low-energy image, reconstruct, from the second sparse-viewdata, a first high-energy image, acquire an artifact-mitigating neuralnetwork that has two channels, one input channel for a low-energy imageand another input channel for a high-energy image, theartifact-mitigating neural network having been trained using a trainingdataset that includes input images that are respective pairs ofsparse-view reconstructed images that have complementary streakartifacts and corresponding target images in which the streak artifactsare mitigated, and apply the first low-energy image and the firsthigh-energy image to the artifact-mitigating neural network to generateartifact-mitigated images.
 2. The apparatus according to claim 1,wherein the artifact-mitigated images include a second low-energy imageand a second high-energy image, the second low-energy image representingthe first low-energy image after mitigation of the streak artifacts, andthe second high-energy image representing the first high-energy imageafter mitigation of the streak artifacts.
 3. The apparatus according toclaim 2, wherein the processing circuitry is further configured toacquire a material-decomposition neural network that has two channels,one input channel for the low-energy image and another input channel forthe high-energy image, the material-decomposition neural network havingbeen trained to generate a first material image and a second materialimage when a pair of a low-energy image and a high-energy image, whichhave streak artifacts mitigated, are applied to the materialdecomposition neural network, and apply the second low-energy image andthe second high-energy image to the material-decomposition neuralnetwork to generate material-decomposed images.
 4. The apparatusaccording to claim 3, wherein the processing circuitry is furtherconfigured to acquire the material-decomposition neural network, whereinthe material-decomposition neural network has been trained using atraining dataset in which material-decomposed target images includecorrections for beam hardening and account for spatial variations in anX-ray beam used to generate the target images.
 5. The apparatusaccording to claim 3, wherein the processing circuitry is furtherconfigured to acquire the material-decomposition neural network, whereinthe material-decomposition neural network has been trained using thetraining dataset in which the target images are trainingmaterial-decomposed images that have been transformed using a whiteningtransform, and the generated material-decomposed images are generated byapplying an inverse whitening transform to resultant images output fromthe material-decomposition neural network.
 6. The apparatus according toclaim 1, wherein the processing circuitry is further configured toacquire the artifact-mitigating neural network, wherein theartifact-mitigating neural network has been trained using a trainingdataset in which target images are generated based on first full-viewprojection data acquired using a low-energy X-ray beam and secondfull-view projection data acquired using a high-energy X-ray beam. 7.The apparatus according to claim 1, wherein the processing circuitry isfurther configured to acquire the artifact-mitigating neural network,wherein the artifact-mitigating neural network is also amaterial-decomposition neural network that has been trained to bothmitigate streak artifacts and decompose spectral images into materialcomponents as an integrated process when sparse-view reconstructedimages are applied to the artifact-mitigating neural network.
 8. Theapparatus according to claim 7, wherein the processing circuitry isfurther configured to acquire the artifact-mitigating neural network,wherein the artifact-mitigating neural network has been trained using atraining dataset in which target images are material images decomposedfrom spectral images reconstructed from first full-view projection dataacquired using a low-energy X-ray beam and from second full-viewprojection data acquired using a high-energy X-ray beam, and the inputimages are spectral images reconstructed from first sparse-viewprojection data acquired using the low-energy X-ray beam and from secondsparse-view projection data acquired using the high-energy X-ray beam,the first sparse-view projection data being at projection views that aredifferent angles than projection views of the second sparse-viewprojection data.
 9. The apparatus according to claim 7, wherein theprocessing circuitry is further configured to acquire theartifact-mitigating neural network, wherein the artifact-mitigatingneural network has been trained using the training dataset in which thetarget images are training material-decomposed images that have beentransformed using a whitening transform, and the generatedmaterial-decomposed images are generated by applying an inversewhitening transform to resultant images output from thematerial-decomposition neural network.
 10. The apparatus according toclaim 1, wherein the processing circuitry is further configured toacquire the artifact-mitigating neural network, wherein theartifact-mitigating neural network has been trained using a trainingdataset in which the input images are reconstructed using a samereconstruction method as used to reconstruct the first low-energy imageand the first high-energy image.
 11. The apparatus according to claim 1,further comprising an X-ray source configured to radiate X-rays havingan energy spectrum that depends on a voltage applied to the X-raysource, an energy-integrating X-ray detector having the plurality ofdetector elements configured to output the projection data, and controlcircuitry configured to rotate the X-ray source and theenergy-integrating X-ray detector through a series of projection views,and change, back-and-forth between a high kilo-voltage and a lowkilo-voltage, the voltage applied to the X-ray source at increments oftwo or more projection views of the series of projection views.
 12. Theapparatus according to claim 11, wherein the control circuitry isconfigured to change the voltage applied to the X-ray source such that anumber of projection views at the high kilo-voltage is different from anumber of projection views at the low kilo-voltage.
 13. An apparatus,comprising: processing circuitry configured to train anartifact-mitigating neural network having two channels by initializingthe artifact-mitigating neural network, obtaining input images includingpairs of sparse-view reconstructed images that have complementary streakartifacts, the sparse-view reconstructed images representingreconstructed images from projection data of a sparse-view kilo-voltagepeak (kVp)-switching computed tomography (CT) scan, obtaining targetimages including pairs of images corresponding to the sparse-viewreconstructed images except with the streak artifacts having beenmitigated, and iteratively updating network coefficients to optimize aloss function representing agreement between the target images andoutput images resulting from the input images applied to theartifact-mitigating neural network.
 14. The apparatus according to claim13, wherein the processing circuitry is further configured to train theartifact-mitigating neural network, wherein the obtained target imagesinclude pairs of full-view reconstructed images that representreconstructed images from projection data of a full-view kVp-switchingCT scan.
 15. The apparatus according to claim 13, wherein the processingcircuitry is further configured to train the artifact-mitigating neuralnetwork, wherein the obtained target images include pairs of materialcomponent images generated by performing material decomposition on inthe sinogram domain using projection data of a full-view kVp-switchingCT scan to generate material-component projection data, andreconstructing material images from the material-component projectiondata.
 16. The apparatus according to claim 15, wherein the processingcircuitry is further configured to train the artifact-mitigating neuralnetwork, wherein the obtained target images further include that thepairs of material component images are generated by performing awhitening transform on the reconstructed material images.
 17. A methodof mitigating artifacts in computed tomography (CT) images fromsparse-view kilo-voltage peak (kVp)-switching CT scans, comprising:obtaining projection data representing an intensity of X-ray radiationat a plurality of detector elements, the projection data representingfirst sparse-view data acquired using a first voltage applied to anX-ray source and second sparse-view data acquired using a second voltageapplied to the X-ray source, the second voltage being greater than thefirst voltage and the first sparse-view data being acquired at viewsthat are different from views at which the second sparse-view data isacquired, reconstructing, from the first sparse-view data, a firstlow-energy image, reconstructing, from the second sparse-view data, afirst high-energy image, acquiring an artifact-mitigating neural networkthat has two channels, one input channel for a low-energy image andanother input channel for a high-energy image, and theartifact-mitigating neural network having been trained using a trainingdataset that includes input images that are respective pairs ofsparse-view reconstructed images that have complementary streakartifacts and corresponding target images in which the streak artifactsare mitigated, and applying the first low-energy image and the firsthigh-energy image to the artifact-mitigating neural network to generateartifact-mitigated images.
 18. The method according to claim 17, whereinthe artifact-mitigated images include a second low-energy image and asecond high-energy image, the second low-energy image representing thefirst low-energy image after mitigation of the streak artifacts, and thesecond high-energy image representing the first high-energy image aftermitigation of the streak artifacts, the method further comprisingacquiring a material-decomposition neural network that has two channels,one input channel for a low-energy image and another input channel for ahigh-energy image, and the material-decomposition neural network havingbeen trained to generate a first material image and a second materialimage when a pair of a low-energy image and a high-energy image, whichhave streak artifacts mitigated, are applied to the materialdecomposition neural network, and applying the second low-energy imageand the second high-energy image to the material-decomposition neuralnetwork to generate material-decomposed images.
 19. The method accordingto claim 18, wherein the acquiring of the artifact-mitigating neuralnetwork further includes that the artifact-mitigating neural network isalso a material-decomposition neural network that has been configured toboth mitigate streak artifacts and decompose spectral images intomaterial components as an integrated process when sparse-viewreconstructed images are applied to the artifact-mitigating neuralnetwork.
 20. A non-transitory computer readable storage medium includingexecutable instructions, wherein the instructions, when executed bycircuitry, cause the circuitry to perform the method according to claim17.